Magnetic Resonance Diffusion Tensor Imaging (abbreviated as DTI) utilizes the principle of anisotropy of free thermal motion of water molecules in tissues to explore microstructure of the tissues so as to achieve the purpose of studying functions of a human body.
Currently, DTI is the unique non-invasive imaging method by which fiber bundles of cerebral white matter can be displayed in vivo. However, signal-to-noise ratio (SNR) of DTI is relatively low, which greatly limits widespread applications of DTI in clinic.
The physical mechanism of Magnetic Resonance Diffusion Tensor Imaging can be represented as:d=Fρ+n  (Formula 1)
wherein, d represents a signal collected by a magnetic resonance instrument, F represents Fourier coding matrix, ρ represents a diffusion weighted image, and n is generally assumed as complex gaussian white noise.
In DTI, the mth diffusion weighted image ρm may be represented as:
                              ρ          m                =                              I            0                    ⁢                      e                                                                      ⁢                              i                ⁢                                                                  ⁢                                  φ                  m                                                              ⁢                      e                                          -                                  bg                  m                  T                                            ⁢                              Dg                m                                                                        (                  Formula          ⁢                                          ⁢          2                )            
wherein, I0∈RN×1 represents a non-diffusion-weighted reference image, φm∈RN×1 represents a phase of the mth diffusion weighted image, b represents a diffusion weighted factor (constant), gm represents a diffusion gradient vector gm=(gxm, gym, gzm)T corresponding to the mth diffusion weighted image, D represents a diffusion tensor, and specifically D may be represented as:
                    D        =                  (                                                                      D                  1                                                                              D                  4                                                                              D                  5                                                                                                      D                  4                                                                              D                  2                                                                              D                  6                                                                                                      D                  5                                                                              D                  6                                                                              D                  3                                                              )                                    (                  Formula          ⁢                                          ⁢          3                )            
wherein, each Di∈RN×1, and N represents the number of pixel points of a magnetic resonance image.
Specifically, the process of diffusion tensor imaging mainly includes the following steps:
Step 1: rebuilding the image ρ by the collected signal d;
Step 2: estimating a tensor matrix D corresponding to each spatial position according to a model tensor (i.e., the above Formula 2);
Step 3: calculating to obtain various diffusion parameters such as MD, FA, or the like from the tensor matrix D, according to the following Formulas 4 and 5:
                                              ⁢                  MD          =                                    (                                                λ                  1                                +                                  λ                  2                                +                                  λ                  3                                            )                        /            3                                              (                  Formula          ⁢                                          ⁢          4                )                                FA        =                                                            3                ⁡                                  [                                                                                    (                                                                              λ                            1                                                    -                          MD                                                )                                            2                                        +                                                                  (                                                                              λ                            2                                                    -                          MD                                                )                                            2                                        +                                                                  (                                                                              λ                            3                                                    -                          MD                                                )                                            2                                                        ]                                            /              2                        ⁢                          (                                                λ                  1                  2                                +                                  λ                  2                  2                                +                                  λ                  3                  2                                            )                                                          (                  Formula          ⁢                                          ⁢          5                )            
wherein, λ1, λ2, λ3 are characteristic values of the matrix D.
In order to increase the SNR of DTI, currently there is a relatively direct method that is to take an average by sampling many times or to reduce the sampling area of K space, and this is called as the 1st method.
Currently, there is still a common method for improving diffusion tensor imaging quality. This method includes: firstly rebuilding a diffusion weighted image after acquiring scanning data of K space; then denoising the image by using a method of signal processing; finally calculating a diffusion tensor and various diffusion parameters by the denoised image. That is, during the process, it is necessary to firstly rebuild the image and then denoise the image, which is called as the 2nd method.